Inverted V-shaped association between atherogenic index of plasma and kidney stone risk: results from NHANES 2011–2018

Introduction

Kidney stones are a common urinary tract disorder that affects a significant portion of the global population. An increasing number of Americans are reported to suffer from urinary stones, with a prevalence of 8.8% in the total population (1), and 50% of patients experience a recurrence of kidney stones within five years of their occurrence (2). As a result of the abnormal buildup of crystalline material within the kidneys, patients with renal stones may experience permanent impairment of renal function (3), requiring a variety of measures for intervention and placing a significant economic burden on patients and society (4).

Kidney stone formation is a multifactorial process influenced by metabolic abnormalities, including hyperuricemia and insulin resistance. Hyperuricemia, characterized by elevated serum uric acid levels, is a well-established risk factor for kidney stones, particularly uric acid stones (5). Insulin resistance, a hallmark of metabolic syndrome, has also been implicated in kidney stone pathogenesis. It affects renal ammonia synthesis and excretion, lowering urinary pH and increasing stone risk (6,7). These metabolic disturbances often coexist with dyslipidemia, further complicating the pathophysiology of kidney stone disease. Understanding these interrelationships is essential for developing comprehensive prevention and treatment strategies.

Atherogenic index of plasma (AIP) is a sensitive predictor of lipoprotein particle size (8) and can be used for the quantification of lipid levels (9). It is a biological marker for atherosclerotic cardiovascular disease (ASCVD) and metabolic syndrome (8,10,11). AIP is closely related to triglycerides (TG) and high-density lipoprotein cholesterol (HDL-C) and not only indicates the ratio of TG to HDL-C but also predicts the lipoprotein particle size and the esterification rate of HDL-C, thus revealing the pathogenicity and specificity of dyslipidemia more accurately. The fractional esterification rate of cholesterol in high-density lipoprotein (FER_HDL) can reflect the function of HDL-C and particle size (12). However, there are limitations in the current methods for detecting fractional esterification rate (FER), time-consuming sample preparation, and the requirement of radioisotopes are the current difficulties (13). Therefore, using AIP as an indicator of dyslipidemia is more meaningful than focusing only on high TG or low HDL-C levels (9). The formation of kidney stones is strongly linked to dyslipidemia (14). A cross-sectional study showed that low HDL-C was associated with a higher risk of kidney stones in an adult population in the United States (15). Similar results were found in a Taiwanese population, where a higher prevalence of dyslipidemia was found in patients with stone formation (16). One study showed a less significant association (17), and another study found no association (18). This suggests divergent conclusions regarding the association between the two. Although previous studies have explored the relationship between lipids and kidney stones (19), the nonlinear relationship between AIP and kidney stones and the population-specific effects remain unclear. Therefore, the present research was conducted to investigate the nonlinear relationship between AIP and kidney stones in adult participants using the National Health and Nutrition Examination Survey (NHANES) database, with a focus on identifying threshold effects and subgroup-specific associations. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-24-605/rc).

Methods

Subjects of the study

NHANES database provided information for this study. NHANES utilizes a stratified probability sampling methodology incorporating nutrition and health data from a representative and diverse population in the United States. The National Center for Health Statistics (NCHS) Ethical Review Board certified the NHANES program, and all respondents or guardians completed the informed document. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Participant examination and questionnaire information is available from https://wwwn.cdc.gov/nchs/nhanes/default.aspx.

This cross-sectional survey was conducted among those 18 years old or older in the 2011–2018 NHANES database (n=9,366). We excluded participants with a body mass index (BMI) below 18.5 kg/m² (n=1,780) due to malnourishment and those with missing data on AIP and kidney stones (n=12,679). Finally, 9,366 subjects who met the criteria were included (Figure 1).

Figure 1 The flowchart in selecting the studying participants. AIP, atherogenic index of plasma; BMI, body mass index; NHANES, National Health and Nutrition Examination Survey.

Evaluation of exposure and outcome variables

The independent variable in this study was AIP, and the level of AIP was determined according to the formula log₁₀ [TG (mmol/L)/HDL-C (mmol/L)]. The outcome variable was a history of kidney stones. Renal stones were defined as subjects responding yes to “Have you ever had renal stones?”. A history of kidney stones was defined as volunteers who responded positively to this question.

Determination of covariates

Potential confounders considered in this study include continuous variables: age, BMI, total cholesterol (TC), TG, HDL-C, low-density lipoprotein cholesterol (LDL-C), glycohemoglobin, fasting plasma glucose (FPG), and poverty income ratio (PIR). Categorical variables: gender, race, education, marital status, drinking and smoking status, and hypertension and diabetes history. There were three levels of educational attainment: below high school level, high school level, and above high school level. Marital status includes three levels: married, single, and cohabiting. Participants who had smoked more than 100 cigarettes in their lifetime or consumed more than 12 alcoholic drinks in 1 year were classified as smokers and drinkers, respectively. FPG, a measurement of the concentration of glucose in the blood after a period of at least 8 hours (usually overnight) without any caloric intake. Hypertension was defined as having a systolic blood pressure ≥140 mmHg and/or a diastolic blood pressure ≥90 mmHg or having been diagnosed with hypertension by a physician and taking antihypertensive medication, and diabetes mellitus is defined as having a fasting blood glucose ≥7.0 mmol/L, or glycosylated hemoglobin A1c (HbA1c) ≥6.5%, or having been diagnosed with diabetes mellitus by a physician and being on glucose-lowering medications.

Statistical analysis

All statistical analyses were conducted using R 4.3.2 and EmpowerStats 2.0, and the significance value was P<0.05. AIP was divided into tertiles. Mean ± standard deviation was used to express continuous variables, whereas percentages were used to express categorical variables. The Chi-squared test and t-test were used to assess the differences between participants in different AIP subgroups and between volunteers with and without renal stones. To discover the correlation between AIP and renal stones, multifactorial logistic regression was used to construct models, with Model 1 unadjusted for covariates, Model 2 adjusted for gender, age, and race, and Model 3 adjusted for gender, age, race BMI, TC, TG, HDL-C, LDL-C, glycohemoglobin, fasting glucose, PIR, educational level, marital status, alcohol intake and smoking status, and history of hypertension and diabetes were adjusted. The non-linear connection between AIP and renal stone was explored by adjusting for variables using a restricted cubic spline (RCS). The data were divided into two groups for separate segmented regressions based on the inflection point of the threshold effect analysis. Finally, subgroup analyses and interaction tests were performed in the regression models to explore whether there were differences between the different populations.

Results

Participants’ baseline situation

There were 9,366 subjects, 4,554 males and 4,812 females. The mean age was 50.10±17.48 years, and the mean AIP was −0.08±0.34. The prevalence rate of kidney stones was 9.99%, including 500 female and 436 male patients. Table 1 is a baseline population table grouped by tertiles of AIP. The high AIP group is mainly composed of older men, non-Hispanic whites, smokers, and drinkers, with more increased HDL-C, lower PIR, BMI, TG, fasting glucose, hypertension, and higher prevalence of diabetes compared with the lower AIP group.

The occurrence of kidney stones was stratified as a column variable. The occurrence of kidney stones was associated with age (P<0.001), sex (P=0.002), race (P<0.001), marital status (P<0.001), smoking status (P<0.001), prevalence of hypertension and diabetes (P<0.001), and changes in AIP (P<0.001). Participants with kidney stones were predominantly male, older, non-Hispanic white, married, non-smokers, without diabetes or hypertension, and had higher levels of AIP (Table 2).

AIP is positively related to renal stones

Multivariate logistic regression analysis found a correlation between AIP and renal stones, and this association was significantly positive in Model 1 [odds ratio (OR) 1.95, 95% confidence interval (CI): 1.61, 2.38], Model 2 [1.60 (1.30, 1.98)], and Model 3 [1.33 (1.06, 1.68)]. Sensitivity analyses using AIP tertiles showed ORs of 1.00, 1.31 (95% CI: 1.08, 1.58), and 1.27 (95% CI: 1.04, 1.54) for Q1, Q2, and Q3 in Model 3 after adjusting for age, sex, race, PIR, marital status, education, BMI, HDL-C, LDL-C, TG, TC, glycohemoglobin, FPG, smoking, alcohol consumption, diabetes, and history of hypertension. Subjects in Q3 were associated with a 39% increased risk of developing kidney stones compared to Q1 (P for trend <0.05) (Table 3).

After adjusting for covariates, the RCS model found an inverted V-shaped nonlinear association between kidney stone risk and AIP level for all participants, as shown in Figure 2 (P for nonlinear =0.03, P for overall =0.007). The inflection point for AIP was 0 (log-likelihood ratio P<0.001). To the left of the inflection point, kidney stone risk increased with increasing AIP (OR =2.08, 95% CI: 1.33, 3.25, P=0.001). To the right of the turning point, the change was meaningless (OR =0.89, 95% CI: 0.59, 1.35, P=0.58) (Table 4).

Figure 2 The relationship between AIP and kidney stone. The solid line corresponds to the covariate-adjusted ratio, and the shaded area indicates the 95% CI, the dashed line is the reference line for OR =1.0 and was used to help determine if there was a significant correlation between the exposure factor, AIP, and the prognosis of kidney stones. AIP, atherogenic index of plasma; CI, confidence interval; OR, odds ratio.

Further subgroup analyses revealed that in subgroups stratified by sex, AIP was related to the development of renal stones only in male subjects (P=0.02). In analyses stratified by age, only the subset ≥60 years had positively associated kidney stones with AIP (P=0.006). The association between kidney stones and AIP also held true in those with diabetes (P=0.01). Interaction tests demonstrated that the correlation between AIP and renal stones was not statistically different between subgroups suggesting that the connection between AIP and kidney stones applies to other populations (Figure 3).

Figure 3 Correlation between AIP and kidney stones in different groups. AIP, atherogenic index of plasma; OR, odds ratio; CI, confidence interval.

Discussion

Although previous studies have examined the relationship between dyslipidemia and kidney stones, the specific nonlinear relationship between AIP and kidney stone risk has not been thoroughly explored. Previous studies have focused on linear relationships or isolated lipid parameters such as TG and HDL-C (15). However, AIP integrates TG and HDL-C, providing a more comprehensive measure of lipid metabolism and its impact on kidney stone risk. Our study identified a nonlinear, inverted V-shaped relationship between AIP and kidney stone risk for the first time. This finding suggests that the relationship between lipid metabolism and kidney stones is more complex than previously thought and may vary with AIP levels. Our subgroup analyses further indicate that this relationship is particularly pronounced in men and those over 60 years, providing new insights into risk factors in specific populations. These findings complement the work of Wang et al. (19,20) and other researchers and provide a more nuanced understanding of the role of lipid metabolism in the pathogenesis of kidney stones.

The association between kidney stones and dyslipidemia has been found in previous studies. The results showed that dyslipidemia was found to be associated with a higher risk of developing kidney stone disease (21-26). Several factors influence the formation of kidney stones, and diseases such as obesity, hypertension, dyslipidemia, and diabetes increase the risk of kidney stones (27-30). Dyslipidemia is widely recognized as an independent risk factor for kidney stones (26,31).

AIP is obtained from TG and HDL-C, so the levels of TG and HDL-C are strongly linked to the underlying mechanisms of kidney stone formation. Some studies have proposed that TG and HDL-C correlate with the levels of physicochemical components in urine. TG and HDL-C may alter the 24-hour urine composition in stone and non-stone populations, leading to urinary stone formation in adults (31,32). Previous studies have shown that the prevalence of kidney stones showed a positive correlation with hypercholesterolemia and LDL-C (24,33). In contrast, the results of our study did not observe significant differences in TG, HDL-C, and LDL-C between the stone and non-stone populations. They only revealed a negative association between AIP and kidney stone occurrence. The inconsistency between the results obtained from the above studies and our study may be due to the following reasons: first, the populations studied had specific differences, such as genetics, geography, lifestyle habits, and ethnicity. Second, the number of populations included in the studies varied. Finally, there may be some limitations in the ability of isolated TG and HDL-C to detect associated diseases (34-36). Although plasma TG or HDL-C concentrations can be used to predict the risk of having renal stones, simultaneous consideration of these parameters (as well as their interrelationships) leads to more accurate results.

The pathophysiologic mechanisms of kidney stone formation are complex, with insulin resistance, hyperuricemia, inflammation, and oxidative stress as potential mechanisms (5-7). There are several possibilities to explain the role of AIP as a risk factor for causing kidney stones. Increased levels of AIP may lead to excess uric acid production, and elevated TG lead to increased synthesis and utilization of free fatty acids, producing more uric acid. Low HDL-C is a potential risk for impaired renal function (37), affecting the excretion of substances in the urine and increasing the risk of having kidney stones (38). AIP may serve as an indicator of insulin resistance, and insulin is an essential factor in renal ammonia synthesis (39,40) and is involved in the activation of sodium hydrogen exchanger 3 (NHE3) (41-43). With the development of insulin resistance, renal ammonia synthesis is reduced, and hydrogen ion buffering is impaired. In addition, insulin affects renal ammonia excretion (17,44). Thus, insulin resistance leads to impaired ammonia production and excretion, which leads to a decrease in urinary pH and the development of kidney stones (45). Previous studies have found that AIP reflects lipid metabolism, and impaired lipid metabolism is a critical factor in the development of stones in kidneys with the presence of Randall plaques (46,47). The association between AIP and renal stones remained robust even after adjusting for different covariates. This suggests that AIP may play a specific role in stone development and that further studies are needed to address these potential mechanisms.

This study’s cross-sectional analysis used the extensive and reliable NHANES dataset to discover the association between AIP and renal calculi. Moreover, the results of this study were validated by sensitivity analysis. This research has a prominent representative nationwide population and subgroup analysis to illustrate the risk of higher AIP on kidney stone formation.

There are some limitations in this study:

• The cross-sectional design of the NHANES did not allow for the determination of a causative connection between AIP and renal calculi.
• Determining kidney stones obtained by questionnaire may have some potential bias, as not all stone formations are clinically apparent.
• Data on asymptomatic kidney stones not diagnosed by a physician should be included in NHANES.
• The relationship between AIP and stone type needed to be clarified. Kidney stones can be composed of various compounds, including calcium oxalate, phosphate, uric acid, struvite, and cystine.
• The NHANES questionnaire did not differentiate between single episodes and recurrent stone formation, nor did it include questions about family history of kidney stones. This limits our ability to evaluate the potential genetic and familial factors contributing to stone risk.
• We overlooked some unnoticed confounding factors that may influence the relationship between AIP and kidney stones.

Conclusions

In conclusion, this cross-sectional study demonstrated a non-linear inverted type V relationship between AIP and kidney stones, with a significant positive relationship between them when AIP levels were below 0. Our findings suggest that AIP can be a valuable biomarker for assessing kidney stone risk, especially in men and older people. However, further prospective studies are needed to validate these findings and to explore the intrinsic mechanism between AIP and kidney stone formation. This study contributes to the growing body of evidence on the role of lipid metabolism in kidney stone disease. It highlights the potential clinical use of AIP in risk stratification and prevention strategies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-24-605/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-24-605/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-24-605/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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